COMPUTERIZED TOMOGRAPHY DETECTION OF MICROBIAL DAMAGE OF PLANT TISSUES

• US 20150300963A1
• Filed: 04/17/2015
• Published: 10/22/2015
• Est. Priority Date: 04/18/2014
• Status: Active Grant

• Current Assignee: The University of Georgia Research Foundation, Inc.
• Original Assignee: The University of Georgia Research Foundation, Inc.
• Inventors: Haidekker, Mark A., Speir, Richard
• Granted Patent: US 9,816,948 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01N 2223/618 food
• CPC Class Codes: G01N 23/046 using tomography, e.g. computed tomography [CT]
• CPC Class Codes: G01N 33/025 Fruits or vegetables
• CPC Class Codes: G06T 11/008 Specific post-processing after tomographic reconstruction, e.g. voxelisation, metal artifact correction
• CPC Class Codes: G06T 2207/10081 Computed x-ray tomography [CT]
• CPC Class Codes: G06T 2207/30128 Food products
• CPC Class Codes: G06T 7/0004 Industrial image inspection

Abstract

The present disclosure encompasses embodiments of X-ray computed tomography-based methods for the detection of onion quality factors. Such methods are advantageous in detecting internal damage to onion bulbs due to bacterial and fungal rots and mechanical damage while also providing for the overall assessment of onion bulb quality and market value. Because CT images provide cross-sectional reconstructions of the subject under study, CT scans of onion bulbs can be used not only to detect damage from disease, but also cuts and bruises that increase an onion bulb'"'"'s susceptibility to disease, and the presence of shoots or seed stems and overall shape of the bulbs.

Claims

1. A method of determining the quality of an onion bulb, said method comprising the steps of:

(a) acquiring at least one CT image of an onion bulb;

(b) adjusting the quality of the CT image to remove noise, enhance the contrast of the image and to enhance the edge of the image of the onion bulb;

(c) identifying the outline of the onion bulb;

(d) identifying the interior voids of the onion bulb;

(e) obtaining a shape description of the onion bulb;

(f) obtaining quantitative measurements of the interior voids of the onion bulb, wherein said measurements are of the relative sizes of the voids, the shapes of the voids, and the location of the voids relative to the internal layers of the onion bulb; and

(g) classifying the onion bulb with respect to at least one of;

the quality of the onion as a marketable product, a disease type generating the voids in the onion bulb, and the extent of the progress of the disease within the onion bulb.

2. A non-transitory computer-readable medium embodying a program executable in at least one computing device, comprising code that:

accesses a computed tomography (CT) image of an onion bulb;

identifies an outline of the onion bulb in the CT image;

identifies a plurality of interior voids of the onion bulb;

generates a shape description of the onion bulb based at least in part on the plurality of interior voids, the outline of the onion bulb, or a combination thereof;

generates a plurality of measurements for the interior voids of the onion bulb; and

generates a classification for the onion bulb describing a condition of the onion bulb based at least in part on the plurality of measurements, the shape description, or a combination thereof.

3. The non-transitory computer-readable medium of claim 2, wherein the plurality of measurements comprise a relative size of at least one of the plurality of interior voids, a shape of the at least one of the plurality of interior voids, or a location of the plurality of interior voids relative to a plurality of internal layers of the onion bulb.

4. The non-transitory computer-readable medium of claim 2, wherein the condition of the onion bulb further comprises a suitability of the onion as a food product, whether a disease exists the interior voids in the onion bulb, or an extent of a progress of the disease within the onion bulb.

5. The non-transitory computer-readable medium of claim 2, wherein the program further comprises code that performs image processing of the CT image to adjust a quality of the CT image.

6. The non-transitory computer-readable medium of claim 2, wherein the program further comprises code that generates a two-dimensional cross-sectional image describing a plurality of X-ray absorption values of the onion bulb.

7. The non-transitory computer-readable medium of claim 5, wherein performing image processing further comprises at least one of:

removing noise from the CT image, enhancing a contrast of the CT image, or enhancing an edge of the CT image.

8. The non-transitory computer-readable medium of claim 5, wherein performing image processing further comprises generating a modified image of the onion bulb comprising only the plurality of interior voids of the onion bulb, wherein the classification is generated based at least in part on the modified image.

9. A system, comprising:

at least one computing device; and

an onion classification application executed in the at least one computing device, the onion classification application comprising logic that;

accesses a computed tomography (CT) image of an onion bulb;

identifies an outline of the onion bulb in the CT image;

identifies a plurality of interior voids of the onion bulb;

generates a shape description of the onion bulb based at least in part on the plurality of interior voids, the outline of the onion bulb, or a combination thereof;

generates a plurality of measurements for the interior voids of the onion bulb; and

generates a classification for the onion bulb describing a condition of the onion bulb based at least in part on the plurality of measurements, the shape description, or a combination thereof.

10. The system of claim 9, wherein the plurality of measurements comprise a relative size of at least one of the plurality of interior voids, a shape of the at least one of the plurality of interior voids, or a location of the plurality of interior voids relative to a plurality of internal layers of the onion bulb.

11. The system of claim 9, wherein the condition of the onion bulb further comprises a suitability of the onion as a food product, whether a disease exists the interior voids in the onion bulb, or an extent of a progress of the disease within the onion bulb.

12. The system of claim 9, wherein the onion classification application further comprises logic that performs image processing of the CT image to adjust a quality of the CT image.

13. The system of claim 9, wherein the onion classification application further comprises logic that generates a two-dimensional cross-sectional image describing a plurality of X-ray absorption values of the onion bulb.

14. The system of claim 12, wherein performing image processing further comprises at least one of:

removing noise from the CT image, enhancing a contrast of the CT image, or enhancing an edge of the CT image.

15. The system of claim 12, wherein performing image processing further comprises generating a modified image of the onion bulb comprising only the plurality of interior voids of the onion bulb, wherein the classification is generated based at least in part on the modified image.

16. A computer-implemented method, comprising:

accessing, by at least one computing device comprising at least one hardware processor, a computed tomography (CT) image of an onion bulb;

identifying, by the at least one computing device, an outline of the onion bulb in the CT image;

identifying, by the at least one computing device, a plurality of interior voids of the onion bulb;

generating, by the at least one computing device, a shape description of the onion bulb based at least in part on the plurality of interior voids, the outline of the onion bulb, or a combination thereof;

generating, by the at least one computing device, a plurality of measurements for the interior voids of the onion bulb; and

generating, by the at least one computing device, a classification for the onion bulb describing a condition of the onion bulb based at least in part on the plurality of measurements, the shape description, or a combination thereof.

17. The computer-implemented method of claim 16, wherein the plurality of measurements comprise a relative size of at least one of the plurality of interior voids, a shape of the at least one of the plurality of interior voids, or a location of the plurality of interior voids relative to a plurality of internal layers of the onion bulb.

18. The computer-implemented method of claim 16, wherein the condition of the onion bulb further comprises a suitability of the onion as a food product, whether a disease exists the interior voids in the onion bulb, or an extent of a progress of the disease within the onion bulb.

19. The computer-implemented method of claim 16, further comprising performing, by the at least one computing device, image processing of the CT image to adjust a quality of the CT image.

20. The computer-implemented method of claim 16, further comprising generating, by the at least one computing device, a two-dimensional cross-sectional image describing a plurality of X-ray absorption values of the onion bulb.

21. The computer-implemented method of claim 19, wherein performing image processing further comprises at least one of:

removing, by the at least one computing device, noise from the CT image, enhancing, by the at least one computing device, a contrast of the CT image, or enhancing, by the at least one computing device, an edge of the CT image.

22. The computer-implemented method of claim 19, wherein performing image processing further comprises generating, by the at least one computing device, a modified image of the onion bulb comprising only the plurality of interior voids of the onion bulb, wherein the classification is generated based at least in part on the modified image.